Modular Random Boolean Networks

TL;DR

This paper introduces modular random Boolean networks, demonstrating through experiments and analysis that modularity significantly influences network dynamics, increasing attractors and approaching criticality more closely than classical RBNs.

Contribution

It extends classical RBNs to include modular structures, revealing the impact of modularity on network behavior and criticality.

Findings

Modular RBNs have more attractors.

Modular RBNs are closer to criticality.

Modularity affects the dynamics of RBNs.

Abstract

Random Boolean networks (RBNs) have been a popular model of genetic regulatory networks for more than four decades. However, most RBN studies have been made with random topologies, while real regulatory networks have been found to be modular. In this work, we extend classical RBNs to define modular RBNs. Statistical experiments and analytical results show that modularity has a strong effect on the properties of RBNs. In particular, modular RBNs have more attractors and are closer to criticality when chaotic dynamics would be expected, compared to classical RBNs.